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artificial intelligence Assisted Student States monitoring based on Enhancing Cognitive and Emotional Feedback Mechanism using Collaborative learning Environments

Artificial Intelligence Assisted Student States monitoring b...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Renuka Tantry Sharada U Shenoy Shreenath Acharya K N Prathibha Department of Artificial Intelligence & Machine Learning St Joseph Engineering College Vamanjoor Mangaluru Department of Artificial Intelligence & Machine Learning NMAMIT Nitte Department of Mathematics St Joseph Engineering College Vamanjoor Mangaluru

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The survey elaborates the specified exploration of rising generation which enhances the pupil’s cooperative problem-solving in computer network troubleshooting by analyzing the present literatures and also add-ons to the discussion about improving learning studies through pedagogical origination and technological integration through synthesizing literature evaluation, methodological development, and empirical evaluation. The effects offer clear cut explanation on how AI-driven structures for remarks can transfigure coaching methods and encourage the continued growth of educational strategies.A preliminary exploration is carried out to scrutinize multimodal data, which includesvarious methods to identify students’ cognitive and affective conditions, methodically distinct educational results with and without feedback systems, and crafting novel techniques for in-the-moment analysis and adaptation of instructional strategies. In the pilot project, a new approach is introduced called Feedback and Emotion based Student State Analysis (FESSA), in which it evaluates the students work in small groups to solve problems cooperatively while their eye movements and emotional reactions are monitored. To understand the emotional and cognitive processes involved in learning, this research based data is evaluated. To examine the effect of feedback structures on learning results and scholar engagement, a comparative analysis is performed. The cause of this assessment is to offer steerage for evidence-based coaching practices in efficient learning *** the end, creative approaches are generatedafter identifying the research gap through literature for the analysis and real-time modification of instructional strategies, emphasizing prompt modifications to student reactions and changing learning necessities. The results of this study are projected to contribute significantly to education on the effectiveness of creative learning strategies and response systems. The mission go

关键词： Surveys Data analysis Federated learning Cognitive processes Education Real-time systems Computer networks

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Unlocking Stock Market Potential: machine learning Predictions for NIFTY50's Most Profitable Companies

Unlocking Stock Market Potential: Machine Learning Predictio...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Preetham Sagar Sanda Revanth Thota Akhil Kodimala Rishab Babu Gadari Gagandeep Aurora Naga Padmaja Dept. Artificial Intelligence and Machine Learning Vardhaman College Of Engineering Hyderabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The use of machine learning (ML) to recognize the stock market trend as well as companies, which are expected to provide the most profit with-in NIFTY50 index is a focus area work in the past 5 years of historical data. We use many ML models (linear regression, decision trees, and neural networks) to predict stock price movements in order to decide which are the top 5 companies with the best return. In summary, according to our experiment the ML models can be good at predicting stock trends and are thus very useful for any investors. It demonstrates how deep learning and machine learning could be used to empower the levels of investment strategies with non-linear irregularities not captured by traditional models. The dictionary tells you what kind of processing has been performed on the data (such as missing value handling, normalization etc.), which keeps things more organized. We also use feature engineering techniques such as moving averages, relative strength index (RSI), and moving average convergence divergence (MACD) to improve the predictability of these models.

关键词： Neural networks Linear regression machine learning Companies Predictive models Market research Indexes Decision trees Stock markets Investment

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Optimizing Marketing Strategies: Predicting Customer Personality using Advanced machine learning Models

Optimizing Marketing Strategies: Predicting Customer Persona...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Adla Aryan Sada Preetham Sagar Nadimpalli Madana Kailash Varma Rangisetti Manikanta Ramojula Rajashekar Gagan Deep Arora Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In the competition of modern marketing, it is highly important to foresee the correct personality profiles of customers because this may further improve the result of marketing campaigns. Therefore, in this research, three major models of machine learning are used, including Random Forest Classifier, Support Vector machine (SVM), and Gradient Boosting Classifier, to discuss the challenges of predicting customer personalities. This study is helpful in finding unique customer segments and appropriate marketing strategies. These algorithms are valid to use for classifying customer personalities, and then determine the efficiency of it using the context also proves that through the more complex applications of such approaches, the efficacy of targeted marketing might be enhanced appropriately. Therefore, even more advanced deep learning techniques would be used at the next stage of research to further improve the forecasting accuracy and applicability in various marketing scenarios.

关键词： Support vector machines Deep learning Accuracy machine learning algorithms Predictive models Boosting Prediction algorithms Forecasting Random forests Optimization

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A Hybrid Deep learning Approach for Accurate and Transparent Maize Plant Disease Classification

A Hybrid Deep Learning Approach for Accurate and Transparent...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： P. Bindhu Madhavi T Subaranjani Sumith Sigtia Vidhan Mehta Dept. of Artificial Intelligence and Machine Learning The Oxford College of Engineering Bangalore India

Within the domain of agricultural progress, accurately identifying instances of plant diseases emerges as a pivotal hurdle. We introduce a fresh approach to classify diseases in maize plant leaves by combining a hybrid model that merges Convolutional Neural Networks (CNNs) for visual recognition with Multi-Layer Perceptrons (MLPs) for transparency. This amalgamation aims to bridge the gap between precision and comprehensibility. Our study begins by scrutinizing the constraints of prevailing CNN and MLP-based models in the context of disease classification. Subsequently, we present our hybrid architecture, elucidating its benefits and addressing inherent challenges. Through rigorous experimentation, we showcase the model’s remarkable performance, achieving training, validation, and test accuracies of 98.98%, 96.19%, and 95.76%, respectively. The significance of our proposed model lies in its potential to transform disease management in agriculture. The fusion of accuracy and interpretability not only equips farmers with actionable insights but also establishes a model for ethical and transparent AI implementation. This research propels the field forward by providing an inventive hybrid solution that excels not only in accuracy but also harmonizes with practical agricultural scenarios, thus paving the path for a more sustainable and enlightened future.

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Flexible Ring Slot Antenna for Optimized 5G Performance in N77 and N78 Frequency Bands for Wearable Applications

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Progress in Electromagnetics Research C 2024年 150卷 47-55页

作者： Soni, Gaurav Kumar Yadav, Dinesh Kumar, Ashok Sharma, Chanchal Yadav, Manish Varun Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science Engineering in Artificial Intelligence and Machine Learning Saraswati College of Engineering Maharashtra Navi Mumbai India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

The growth of 5G communications has created a demand for advanced wearable and flexible antennas due to supporting the high speeds, low latency, and capability of mechanical deformation conditions such as bending and conformability. In this paper, the design and analysis of a defected ground structure (DGS)-based ring slot antenna for N77 (3.3–4.2 GHz) and N78 (3.3–3.8 GHz) frequency bands is demonstrated. The antenna is made of an RT/Duroid 5880 substrate and has a loss tangent (tan δ) of 0.0009 and dielectric constant (Εr) of 2.2. A DGS-based ring slot microstrip antenna is simulated, tested, and experimentally characterized on different body locations (leg, chest, and hand) along with bending, and their results are presented accordingly. The magnitude of S11 (|S11|) of the proposed antenna is −26.81 dB at resonant frequency of 3.45 GHz, with the impedance bandwidth of 22 MHz (3.486 GHz to 3.508 GHz), peak gain of 6.27 dBi, and radiation efficiency of 85.02%. The simulated specific absorption rate (SAR) for 1 g and 10 g human body tissues is 0.263 W/Kg and 0.076 W/Kg, respectively. The total volume of the antenna is 0.58λ0 × 0.58λ0 × 0.00595λ0 (at 3.5 GHz). The proposed antenna is suitable for 5G wearable devices. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Microstrip antennas

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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SkillSync: Evaluating Candidates for Communication and Problem-Solving Proficiency

SkillSync: Evaluating Candidates for Communication and Probl...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Nadimpalli Madana Kailash Varma Sri Harsh Mattaparty Durgam Nikitha Mohammed Talaha Numan Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telanagana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In contemporary work environments, effective communication, collaborative problem-solving, and positive attitude are pivotal for success. However, conventional hiring approaches often struggle to accurately evaluate these essential competencies. SkillSync presents a groundbreaking solution to this challenge by introducing an innovative assessment platform tailored to measure candidate’s communication skills, problem-solving abilities, and positive demeanor. This project investigates the development of a comprehensive assessment tool designed to analyze an individual’s disposition and adaptability across diverse workplace situations. The tool will leverage a user-friendly interface and a novel algorithmic approach to evaluate an individual’s inherent characteristics. The algorithm will focus on analyzing the relevance and efficacy of candidate responses, with the primary objective of furnishing employers with valuable insights into a candidate’s content writing proficiency and their capacity to approach workplace challenges with a positive outlook

关键词： Employment Focusing Collaboration Writing Position measurement Problem-solving Standards

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Virtual Scribe for Visually Impaired Learners Through Interactive Speech-Based Application 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Sanjeetha, R. Peter, T. John Kallimani, Jagadish S. Sambhram Institute of Technology Bangalore India Department of Computer Science and Engineering Sambhram Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Blind people face various challenges every day, something that people take for granted. One such problem that blind students face is the need for a scribe to write their exam. The onus falls on the blind student to find themselves a scribe which in itself is a difficult task. But even if a blind student has found a scribe, the scribes come with their own set of problems, such as linguistic capability and illegible handwriting. To solve this problem, V-Scribe or virtual scribe is being proposed which is a web application that would completely replace a human scribe. The application uses speech recognition, text-to-speech and a natural language understanding module to understand the requests given by the blind student and give back an appropriate response. The DIET classifier developed by Rasa is trained, obtaining an accuracy of 96.7% for intent recognition and 94.7% for entity recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Sentiment Analysis of machine learning Algorithms: A Transformer-Based Approach

Sentiment Analysis of Machine Learning Algorithms: A Transfo...

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Intelligent Systems and machine learning Conference (ISML)

作者： Nadimpallli Madana Kailash Varma Sudabathula Vijay Sai Kumar Sri Harsh Mattaparty Shifa Ismail Marisetti Harshini Syed Ahmeduddin Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Transformers library. Researcher’s comments from published papers were analyzed using the Transformers library, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Neural Networks consistently outperform other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection.

关键词： Support vector machines Sentiment analysis machine learning algorithms Systematics Neural networks Transformers Libraries Vectors Robustness Splines (mathematics)

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